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Learning to Enumerate

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9886)

Abstract

The Learning to Enumerate problem is a new variant of the typical active learning problem. Our objective is to find data that satisfies arbitrary but fixed conditions, without using any prelabeled training data. The key aspect here is to query as few as possible non-target data. While typical active learning techniques try to keep the number of queried labels low they give no regards to the class these instances belong to. Since the aim of this problem is different from the common active learning problem, we started with applying uncertainty sampling as a base technique and evaluated the performance of three different base learner on 19 public datasets from the UCI Machine Learning Repository.

Keywords

  • Active learning
  • Learning to enumerate
  • Exploration vs. exploitation
  • Epsilon-greedy

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  • DOI: 10.1007/978-3-319-44778-0_53
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Acknowledgments

This research was supported by the Landesstiftung Baden-Württemberg (Baden-Württemberg-STIPENDIUM) and by MEXT Grant-in-Aid for Scientific Research on Innovative Areas.

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Correspondence to Patrick Jörger .

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Jörger, P., Baba, Y., Kashima, H. (2016). Learning to Enumerate. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_53

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  • DOI: https://doi.org/10.1007/978-3-319-44778-0_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44777-3

  • Online ISBN: 978-3-319-44778-0

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